Recently, kernel Principal Component Analysis is becoming a popular technique for feature extraction. It enables us to extract nonlinear features and therefore performs as a powerful preprocessing step for classification. There is one drawback, however, that extracted feature components are sensitive to outliers contained in data. This is a characteristic common to all PCA-based techniques. In this paper, we propose a method which is able to remove outliers in data vectors and replace them with the estimated values via kernel PCA. By repeating this process several times, we can get the feature components less affected with outliers. We apply this method to a set of face image data and confirm its validity for a recognition task.